Zobrazeno 1 - 10
of 1 515
pro vyhledávání: '"P. Callot"'
We propose a new method to measure the task-specific accuracy of Retrieval-Augmented Large Language Models (RAG). Evaluation is performed by scoring the RAG on an automatically-generated synthetic exam composed of multiple choice questions based on t
Externí odkaz:
http://arxiv.org/abs/2405.13622
In large IT systems, software deployment is a crucial process in online services as their code is regularly updated. However, a faulty code change may degrade the target service's performance and cause cascading outages in downstream services. Thus,
Externí odkaz:
http://arxiv.org/abs/2401.10338
Autor:
Januschowski, Tim, Gasthaus, Jan, Wang, Yuyang, Salinas, David, Flunkert, Valentin, Bohlke-Schneider, Michael, Callot, Laurent
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organize
Externí odkaz:
http://arxiv.org/abs/2212.03523
Autor:
Karthik, Enamundram Naga, Kerbrat, Anne, Labauge, Pierre, Granberg, Tobias, Talbott, Jason, Reich, Daniel S., Filippi, Massimo, Bakshi, Rohit, Callot, Virginie, Chandar, Sarath, Cohen-Adad, Julien
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent years. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset
Externí odkaz:
http://arxiv.org/abs/2210.15091
In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We propose SpectraNet, a novel multivariate time-series forecasting model th
Externí odkaz:
http://arxiv.org/abs/2210.12515
Anomaly detection in time-series has a wide range of practical applications. While numerous anomaly detection methods have been proposed in the literature, a recent survey concluded that no single method is the most accurate across various datasets.
Externí odkaz:
http://arxiv.org/abs/2210.01078
This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing window-based methods, it
Externí odkaz:
http://arxiv.org/abs/2205.15548
Autor:
Alexis Bernard, Anne Battani, Andrea Luca Rizzo, Uğur Balci, Domokos Györe, Walter D’Alessandro, Jean-Paul Callot, Konstantinos Kyriakopoulos, Magali Pujol
Publikováno v:
Frontiers in Earth Science, Vol 12 (2024)
Santorini Island (Greece) is an active volcano which has alternated between dormant and active periods over the last 650,000 years with the latest volcanic unrest occurring in 2011–2012. Here we report a geochemical survey of fumarolic gases collec
Externí odkaz:
https://doaj.org/article/35d71bf902c640dab8b324b8b4ddce9f
Autor:
Minorics, Lenon, Turkmen, Caner, Kernert, David, Bloebaum, Patrick, Callot, Laurent, Janzing, Dominik
This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by
Externí odkaz:
http://arxiv.org/abs/2202.11612
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets. Among reconstruction-based models, most previous work has focused on
Externí odkaz:
http://arxiv.org/abs/2202.07586